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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Centerline Usage Example\n",
      "\n",
      "This example show how to intialize a centerline using a shapefile and to add point river location measurements.\n",
      "\n",
      "Below are the Python modules required for this example:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from os.path import join\n",
      "import numpy as N\n",
      "import pysal\n",
      "from SWOTRiver import SWOTL2\n",
      "from Centerline import Centerline\n",
      "\n",
      "# For plotting\n",
      "\n",
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Read the example data\n",
      "\n",
      "In this step, the data are read, a projection to (x,y) coordinates is defined, and the data bounding box is found.\n",
      "\n",
      "The data locations are given below:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# This is the example data\n",
      "\n",
      "data_dir = '../../data/examples/'\n",
      "l2_file = join(data_dir,'simulated_swot_test_data.nc')\n",
      "centerline_file = join(data_dir,'sacramento_centerline.shp')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Read the simulated SWOT data\n",
      "\n",
      "A simplified version of the SWOT simulator data containing only latitudes, longitudes, water classification, true and simulated noisy height is provided in the `data/examples` directory.\n",
      "\n",
      "For this example, only data with classification=1 labels are used (pure water pixels) and the latitude and longitude are assumed to be known from the reference interferogram. The following lines read the latitude-longitude data for the water pixels, and projects it to a Lambert Equiarea projection."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "class_list=[1]\n",
      "lat_kwd='no_layover_latitude'\n",
      "lon_kwd='no_layover_longitude'\n",
      "\n",
      "l2 = SWOTL2(l2_file,class_list=class_list,lat_kwd=lat_kwd,lon_kwd=lon_kwd)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Dataset opened\n",
        "Bounding box calculated\n",
        "Good data selected\n",
        "lat/lon read\n",
        "projection set and x,y calculated\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Read a candidate centerline\n",
      "\n",
      "A candidate centerline for part of the Sacramento River is provided in the `data/examples` directory courtesy of [Tamlin Pavelsky](mailto:pavelsky@unc.edu) and [George Allen](mailto:georgehenryallen@gmail.com) and modified by [Ernesto Rodriguez](mailto:ernesto.rodriguez@jpl.nasa.gov) so that reaches appeared as topologically connected.\n",
      "\n",
      "The shapefile is read using the pysal package and the latitude and longitude are extracted."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "shp = pysal.open(centerline_file)\n",
      "cline = shp[0]\n",
      "lon, lat = N.asarray(cline.vertices).T"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To get true distances, the centerline must be given points so that the Euclidean distance can be calculated. Below, the SWOTL2 projection function is used to project to the same projection as the SWOT data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "xc, yc = l2.proj(lon,lat)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###To see what has been done, plot the centerline points and the measurement locations\n",
      "\n",
      "The centerline is plotted as black points, while the water measurements are plotted as blue points. As can be seen, the simulated measurements have some gaps over the river and also include water points outside the river. Notice also that the measurements show meanders not present in the candidate centerline: this defect is corrected by the class IteratedRiverObs, discussed later on."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figsize(6,6)\n",
      "plot(l2.x/1.e3,l2.y/1.e3,'.b',alpha=0.01)\n",
      "plot(xc/1.e3,yc/1.e3,',k',alpha=1)\n",
      "xlim(-5,5)\n",
      "ylim(-10,10)\n",
      "xlabel('X (km)')\n",
      "ylabel('Y (km)')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<matplotlib.text.Text at 0x10b97c410>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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jBX19FEIAXUTFxdKobjJJJqkQUilr9KjLxf5Py5czwysQ4KWmxpovYSyJS0GU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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b975290>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Initialize the Centerline instance\n",
      "\n",
      "The following step shows how the Centerline is initialized with default parameters."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "centerline = Centerline(xc,yc)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The following step shows how to associate the simulated measurements with centerline node locations and assign along-track and normal coordinates to each point. *(Note that instead of calling the instance, the member function to_centerline could also have been used.)*"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "index, distance,x,y,s,n = centerline(l2.x,l2.y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot the distribution of distances to the input centerline nodes (*distance*), as well as the normal coordinate (*n*), for each of the SWOT simulated data points."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figsize(10,5)\n",
      "subplot(1,2,1)\n",
      "hist(distance,bins=100,log=True)\n",
      "xlabel('Distance to node (m)')\n",
      "ylabel('N observations')\n",
      "grid();\n",
      "subplot(1,2,2)\n",
      "hist(n,bins=100,log=True)\n",
      "xlabel('Normal coordinate (m)')\n",
      "ylabel('N observations')\n",
      "grid();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Rzc3NmDlzJoYNGxZE6PkA1gbxfiJSkdPphNPp7DQndcaoHGZG/pKtU0pE5gg1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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ba005d0>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Notice that most of the data points are close to the centerline, but, as expected from the data picture, some of the points are far away. These points can be filtered using the RiverObs class. \n",
      "\n",
      "Below is a zoom around the centerline."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figsize(10,5)\n",
      "subplot(1,2,1)\n",
      "hist(distance,bins=arange(0,500,10),log=False)\n",
      "xlabel('Distance to node (m)')\n",
      "ylabel('N observations')\n",
      "grid();\n",
      "subplot(1,2,2)\n",
      "hist(n,bins=arange(-500,500,50),log=False)\n",
      "xlabel('Normal coordinate (m)')\n",
      "ylabel('N observations')\n",
      "grid();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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GmM1mDB06FBMmTEBjY6PyXPZfRKQq0QMHDx4Uc+bMEUFBQS49/4MPPhB79uwR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\nQCvzt+NhTl2gkilvTCuxMJdN6/v5os6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9e5yWliY+//xzIUT7WqulpaWdjstRX/b888+L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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10cf35050>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Below is a plot of the normal coordinate ploted as a function of the reach distance along the centerline. Where the centerline and the river measurements agree, one can estimate the river width. The missed meanders in the centerline are also easily identified."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(centerline.s[index]/1.e3,n,'.',alpha=0.1)\n",
      "xlim(40,90)\n",
      "ylim(-500,500)\n",
      "xlabel('Centerline Reach (km)')\n",
      "ylabel('Normal coordinate (m)');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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QunV04J/PN/W0TR4PfIF6erApqNXj3wcWC76LCILiaJ3QJfGPwYCxZTROrIDu\n9+P78vPhAqDVwlxaWire8+67oqpDcH63eMVkglY8PZ1o1izxem0tgow4P2VnZ2LOqREFt6985Sv0\nwgsv0IIFC2RBd4kkgfjbvxUm0qVLib74xcg+v2ABTtbBUWt9ffjp8aD26Xhqm+INLvel0WDhn4io\nNDZjKQpMPpLJy/z50IyXlIwtjU8kGI34qdcj5cesWdBKrVqF9jC9vThAxLubBJfmamuDpnrevIEu\nP2o15lRq6tgSF8eaEQW33NxcuvXWWyeiLRJJXJHoZqobbiD693/HCfOOO3CSjoRQUWtqNRY8RZka\nqTCCx4BaDSGKURT4JFVVXZ3oM1oEAtB0RCMCLtHH82SnogJat9EmzB4NubkYF1lZMPvPmIHvX758\nYBuuvRalsCoqUD4vXnniCfieNjejrT4for/Z08vnEz6qra2TWHBbvHgx3XXXXXTLLbeQ7tOaOTId\nyORELuwDYWdhRYH/x0T4nEQTtZrouutG//lQASr5+fAfmSqpMDif4SefEL3/PhZ9gwGah/R0otOn\n0Q+siQvWUsQbbHZVFGl2jUfGmjA7XIKrCgSP57NnoUlXqSDABbu13347tFQbN8a3f1t7Ow6WbW2Y\ns4EAku8G34ui4HW3e/QVJ2LNiI+gt7eX9Ho97dq1a8DrUnCbfMikuwOxWGAa9PmIiosTT3AbDxQF\nAstUEeo5n2FVFeaE0wk/JK0Wm5xajZM7Ufyf3r3eqembKBlIsI8X1yVVFKJTp5BcOxCAoBMs7Gg0\n8a1pY2bMQCS9Xg8tYnf3wL+zX6qiEKWkTMLKCcxvf/vbCWiGJB6QSXcHoihYyFJTE3eCS8YG5zPU\n65FGwOGA47ZajUg8ux3CkNksxshQdRJjjU4HTUR2tpzfk53hxqDfj3W+pQUHU/b1Uqnw/9TU2LV7\nrDzxBNG2bXAtaGjAfZlM4u+5uRj7Gk1iV04YcknZtm0btfJRMgQWi4W+//3vj0ujJLFBo8Fkj2dV\n+ESSkYHNLpHz/USbxkYsiBcvQmCZ7LC5eOlSmJQWL8ZPpxOpUjIyoIE0mfB/Imguzp4lOn4cebLG\nQnMzgkDq6sauKfP5IHAGAhBGJZOX4cZgWRm0Ta2tWO/tdhxI5s+HK8TSpShtlYgYjUSPPQbBzWiE\n4BpcT3jaNKRamTkT95io6/qQW/SyZcvozjvvJLfbTUuWLKH8/HwKBALU0tJCVVVVpNfr6R//8R8n\nsq2SccZBSJCYAAAgAElEQVTlgtbA4xloKj18mOjkSZzMli9Hcsh40SKMJ4qC02eiTu7x8Fns6MA1\n7XZsClx8PhZMZG6pDRsgRM2dS/TBByJFh9UK4b67G0IcEQStri709+LFY/ve1lZsPC4X8lFFkhB1\n8PP3+TCePZ6xtUkS/ww3Bpua4At26ZKIFu3uhlBTWorqAono9xUMByF4PAMDeziKVqcT5tREZMil\n7uabb6abb76ZGhsbad++fdTQ0EBEKA7/0EMPUZGMTZ90DGUq7ewUE/yTTzD4JypcXTJ6+vuxgNts\nOGGvWTN2gdvlEukpiouj0sxRc+ECDhMuF9GuXUSf//z4fZdajRN6UxP+bzRCc8Wn+tRUbJRmM17v\n6IiO839bGwTG5OTII0sH+6xKU2l8E82DVnIyTKE5OVdfx2rFWEpPh2mUi8ZrtcjPFqrMIafZ0OlQ\nCiveC82npGBuZmQM1LidOYP1Yu9eoupqoh07YF41GGLW1FEx4hm1uLiY7rzzzoloiyTGnDyJzTAn\nBzm8nE5MZo1GODVXVAws2isB8biwqdVEb7+NdqWnQ5u6YMHYrpmRAVNpZmbsta4qFQ4UKhXRkiXj\n8x3Bm6nXi5/8vRkZENI8noEat5QUCHLR0Nb6/bi20QgBPBKCD2KKgsz8bW1CsJTBNvFFNIPDpk/H\nuCwouPo67Ju5Zg3R889DsNHrIewN9Z0HDuB9fj/2hDVrRt+2iSA/H/eYl4eABaaxEQKt3Y577egg\n+tnPiB55JHZtHQ3Sm0lyhbY24avzzjuYnE4nJoHBgI1p3rzYb9gTRXc3Nkufb+SIQfYZstuJDh1C\nAstYk5srMrH39KD+4FgFN47Gigfz8ec/T7R79/iadnbswEKv16OSBGspPB6xwbLpsbMTGrfhNs1I\n6enBtXp6Io9a5cAKRYFZ7PJlBFi0tEAglYJbfBHN4LBQybOZtWuxRq1YAaHm4kVxMDh5MnRADY95\nr3ds7RpvSkthBnY6iebMQUmr9HTxd51O9HF/P9ayb387du0dLVJwk1xBr4fQptEg/xeX9ZlKwlow\nHR3Y4FwubIDDCQd8itVo4sexV1HgiFxTA2HiS18a+zVdLix4fX2xTX9x+DCeT3o6KgqMhyD5u9/h\nNM4b1vTp2NRsNiz+HR0QhIxGjA8OThhu04wUzjc1mr7mwIr9+2EW+vhjvLZ8OdomiS9Y0A5lqoyU\nUMmzGbVaHCxTU7Hua7UYzzt2EP35z0Rf//pAn9ElSzDn5s+Pj0PpUFgsYq+qqYGy4bXXiP7+7/Ea\npwMpKUF///CHiWcmJZKCmySITZugwdi4EYP//PmJzeIdb3AUXzibZvApNh7MpMy99xK98grR5s2R\nV04Ihc8nSj9x7cJY0NlJdPQoTtenTxM99FD0gxMaG3GvLpfQUFks0EyrVOI52+1Y/Nvb0S/DbZqx\noKYGLhA1NfBtqq8nOngw/s1dU41QCa/H+1oaDcauXo/5NHMmPrdz58B6pBs2wJQab+vbYDQauDEw\nly4NDMbJzYWbR3Y2NPWJKLQRDZMOhDl37hzdcMMNVP5pNr4TJ07Qj3/843FvmGTi4SSLGo3I4j1V\nhTYinNbUakz0kUwXfIqNt0VNqyW6887oCG1EYpE3GgeaICYanQ6BMmfOQBDZuTP632EwCDeBkhJU\ni+B8bpwKJTsbG4VGA/NMTQ1MTw0NSMkw1hQeZ85A6KquHn00qNcLs79KhbbLqFIJEXK9NTVhbLhc\nmFOZmRjbgwNh4nV9G8zrr2NdYp9eo1HUVyaCxrywECbS4uLEzc854rZ8//33009/+tMr5a4qKiro\n97///bg3TCKJNRkZ4nQWrubEaoUvUSLXwRsOsxmbf3DC2Viwdi00XN3dEJbGw/fmG9+AefnBB4me\nfhrpEr75TfiuabXoh6QkYSavrsaGEY08bjyOGhogELa1YVOKhNpaVHwIBDCW09LgK5eRET/mfEns\n2LUL48zhgDl+/nyYTq+/nmj16li3LjIOH4ZfNhHRRx8hF11SEsb6NdfABPyf/4l7zcmBC4nbjbUj\nEQmr5NXKlSuv/F9RFNJG6/guiStkrdKBjCaPW6LXNx2J9HQsfBkZsR0fajUEkYsXRTRntNHpiL72\nNfzu90PrOns2TEZ6Pf65XCKJaUEBNoJTp2DKVasxBkYD1xVlR2tOORIIhN/vXNpIr4fWMDsb/nk9\nPUTPPQdBNJ6ioCUTy+HDGAv9/fh3ww3QRsW7OTQUdXXQJLtcGOsFBUijwzWVL1/G+7q7cQgqLMQc\nTdTD9YiCW05ODl24cOHK///3f/+X8vPzx7VRktjAmwWRKEKdKMJcvJQZqqnB4hAIwGl+sgluRBAE\n4gHOjq7Xw+TT3AyTZW4uAgWef16U9bnhBphHRiukBBcAT02F8JaejvnBAtKFC0KQs1qhkeSAhUjp\n6IDAptFgLKnVRMeOQZALd0zZbGgLB6lYLLiexyNyW8VTFLRkYklOFr+rVBgniTwO+vtxDzNnYh6a\nTHjNZsMaYbVijcjIIHrpJczjhgaiRx+NnzUtXEYU3H7xi1/QV7/6VTp37hwVFBRQaWkp/fd///dE\ntE0ywXg8V6e+SJTC87x5+nwQ3ubNi007bDac7pKTE/c0NxwdHfgXD8XKKyogLBEh1cmcORifKhVO\n1cePw4TKZboWLcLvd98dvmD/1FNwcDYaib7zHSzwGg3GmEoFId3thgDU0YHvSUoSfTNabWAggGsa\njRDgdDocBCJJ46FWw2RfWIhDWFIS2lhRgcMN++ZJs+nU5NpriV54QUSVNjfHukWjp6wMUdOVlRjr\nLhf+sX/b1q0I0mpvRxDGuXO458ZGHLC+973Ytj9SRhTcVCoV7dmzh3p6esjv95PJZKK6urqJaJtk\ngjGboaHIyhICWrwXnmeNYF0dNlKVCv4N0bim1Rqe8BVcesliwUafmRl7wYYo+ppIXgg9HgQFVFSM\nuYmjwmpFioL334f5sr8fSUJXrICA09GBtrL/jssFjZVaDXN2WdnI31Fbi43N6RTmxocfxk+dDj9t\nNvEdHg+EJIsF/W0yjV1DnZ0Nc1ZmJgIk5swJ/7Mejyhx1dWFw4TJhLYXFEDoTESz2GRloq0bmzcT\n/eM/4nDjdGI+D5XHLZ45fFj4kj7wAPaw5maR6iQQEEFar72G+cnJtJ1Oon37iN57L7FcBkYU3G6/\n/XY6evQopaSkXHnti1/8Ih05cmRcGyYZPaPdrPPycAIPTmPAuYVsNpxUYm2KHAybdw0GbNTRyDnH\n1+zvF2kdbDYx2Qcvqvv34yTndsNkl5mJ9126FDvBhmEHZBZqPw0OHzVuNwQJjQZ5zWLF7t3o85QU\nmEwNBphIWNAOBJDu4tAhJOp9/nk8y6oq5CgMhx/8AGkz/H4874oKLPB9fXiNE3hyihSvV/jS+HzY\nFEardU1Lg8CVno7I1oyMyKO8OW0L5yO8dAnXdbsH5vKKNoniXhFvsH+sWo0awOPtZqHR4KDh92NM\nnD5N9Oab0VknJpIDByCo+XwITNBqMXf6+jB3giuO+P14/dNYS8rLgwBbXw8hL1GCMoYU3M6cOUPV\n1dVkt9vp1VdfpUAgQIqikMPhoH62n0niEs6v5XQiF1u4dUW7ujCB29rEgsv5gD4tVRvxNccbNu+m\np0NjWFgY2pwbyWbC/kV+P4SwggKcRG02fH5wpn63GxGFDgc26rQ0CHtj1fxFA4cD/dPbi8VprAty\nZiaE0+zsgTUAJ5pLlxCYwALy3XeL6hVMRgbRTTeJQvB9fdA6hdvuy5dFws5AgOiZZ1DjMCsLG4XF\ngtfZbNzbiwhUnn9bt47exy0/H2M0JwfjNi9v+CjeUOO7t1fknePDh9OJuTueZetCCSA7dqA/9Xr0\ny2SObxvtwbmqSoyX/PzoC26hyvLl5mJ8EGEcHziAcfKTn0Q/L+J4UVMzsIJJMDbbQMtHcjKex8yZ\nuP8tW8TzSaSk1EM+mpqaGnrjjTfIbrfTG2+8ceX11NRU+s1vfjMhjZOMjpYWaCN0OqJly8L/3FD+\nbI8/jpxZGg3RXXfBFNXaGh+naTbvLlgAf6ChUnfwvbW2CsF0qPazf1FGBoS2oiKo0/m0Vls7UHBb\nu5bojTeE9qWjg+izn40PreSMGdhIZ84kuvHGsV/PZBICBPtDxmIMpKeLNCDl5ch3NnijUxQI0U1N\neI4tLdgQb7opvO8oKAhdoLujQ/i5ud0YWwYDvuv8eWwIPh8qL/zf/zu6+2Ptt04nouWG096xlthm\nQ7+YTBinPh8OXY2NaHtREXzdzp9H+odICPfww8Jifz8EtY8+InrxRdxLeTl8jSZz+WvWcvf3I3Ft\neXl4a2VfH7TZXMFmMGPVZHK9UfYDXrMG8yg1FWPXZoM5vrQUGu3PfS6y648H4dxzQQEOKdOnw33i\n1CmsvSkp6EsWTIlwYKmuxlhcuhT3WF+PzyVSINmQgtumTZto06ZNtH//flqdKPpDCRFhAPb3Q6ip\nrQ1fOzaUP1tdHQSipiaiV1/FpI+XdBehzLuhJjvfW0cHFqnmZhE5OxTcD4qC6zQ24mdQdhwiQt6j\nOXPwvsZGbI4228hlsiaCL3xBVMOIxgm6pAQlcaxWbFBz5kAYmihYmzFjBjYhgwGvDaWh4Gc/bx4W\n8S99Cc8lnI3vl7+E+chuhzBChPGwcCGEEUUR/jNeL8aTXi8OCXfdNfrqEqzprq1Fe51OaBjXrw/9\n/s5ObPpnzuCAkZyMz7LmjbXnSUkYo6MJSAg3UKmnB5ujXg+h5Ve/gqDI0ax33x0/B7/x4OJF3GdH\nB4SGadOwFm/dOvQcZCG7t5do1qzQGtGR+n8kTV9vL4R7Th69YwfcPBwOrFOFhTjoGwxYL+KBUJkO\nBrN8ObSVfX0we86YITTiqakD1+uVK2E90ekwj/V6uE7ES5WTcBlxKV+8eDH94he/oOrqaurr6yPl\n07t75plnxr1xktGh1+MUolZHZhLRaKCpG7wgJCVhAcrJgZDS3o7JX1wce8EtVGmXUAsc++qpVNg0\nFAUCVijYvyjYuXzduqFLWqnVRPfdh+z9SUlY+Orr0U+x0kgxXA0jXEY64Wq1WOg1GmwUf/4z0Ze/\nHN02DwdHD3u9oi1EEGxsNqEFTE8XzvkLF8KcunixKDIdToR0UhI2tp//HEk8t22Dtuhb3xLO/Skp\nMAF6vdg4fv5z5H7buBHzpakJiUFHmy+trQ2bMVdlCEVtLdrAGfBZw+D14h7S0kQNYocj8iAHxuGA\nUKZWI3pvKNgclZxM9PbbECYVBc9m5kyhqRz8DOIlpc9YyclBP7G2l30x8/IGlpEKxm5H3zgc0GiH\nElAaGvActVpo8gbDQhgRntHgAJxly2AunTcP/o3/8A94v8OBPeO++6C1itYhLxqw28pQ6/Xhw5jn\nTU1o+4kTSP2zfz/6qqBg4Dhavx5+q6mpeD04EC+RGHFq3HPPPdTa2ko7d+6k9evXU2Nj44BABUn8\nUV4OM9/SpZEtfi4XFg2PZ6C/0I9/jFPgf/4nFt9Ll7BJjlfi03AZXKWA/3/xIiZyZ6cw6wULeHb7\n8Gan/HwsnAUFAz8zfToWklCfveEGaHNmzBCLdWsr3p8o1NYiQvajj6CVCdX2hgYIBDYb+mG4DXw8\nYEdqvx/jj/3PWlqwUFuteO5mMwTPwkKM1wsXsMgnJ4cfIf2736EIdXc3nv3u3aL6gE4HTQoR5oTP\nh3bl5BA98gg2arVaOEZzvrRI6enBv+5u3F+oSOWGBmy+ra34vo4OjEG3G+4DwSZXIlznrrugnfir\nvxpY23EwwXOMNWaBwNX+hMG0teF99fVEH34Igba9HRvvgw+KYI7Bz+C115Bf6/e/h8YuUdm4EQfm\n228XSZQ5knEw3L+c+y8lBf9Czb3eXiGcnz9/9d97evDMe3tDm1rXr8fh4eabMTb5EBQIQMCvrBQl\nD+MFkwmCamZm6APwgQNYr9raILR1dEAwZf9QooFab7UaAQh6fWJrfEfc1i9cuEA/+tGPKCUlhbZu\n3Upvv/02HTx4cCLaJhklKhUm37FjUAuHm5aCfXMGL6oGAzYjgwGnxpwcbFrB0Tqx4OxZ3N/x49BG\nHD8O01ZTEwQPn2/4DaarK3R5qrw8bDJFRWJiWywizDzUospRet3dWCja22FiTqRcbqdOYUM4exZC\nXCisVmha9XpoIcNJqzHUdUZTGqysDBtbZSU0aVotPn/xIp55Vxf+z6afoiLcz/nzGCvPPCMc/0fi\nl7+Eme/JJ4nuuUeYS4nweY1GaJICAVG/1evF7+wT2dc3+nxpJhOuHQig/WfOXP0emw0bV08P5mZr\nK76zvh7P0WaDFlWjwb+eHvRVTw/G6UMPDf39g7XXnF5kOPr7hUBgtYrakJ/5DNYNoxGvDX4GJ07g\nOZ0+jQj2RKWyEhHIeXnoC/b3G1z/k0j074wZSGnh8UDAb2+/+r2BAMZWRwd+Dp43c+dCQJ87Fxri\nwQyuN1pSIq6r1YZuX6wJdYhmamtxIPv4Y6FpzsnB3lVcjM8WFw/cyw4fxqHg29+GG8ns2VjXE40R\nZWuuUZqWlkYnT54ks9lM7aFGlSSuGE1kKZsTh7P35+djomdmjs7cMhoGm++6uvB/9qPx+7HQnT+P\n06bHA21jWxsm7lD092Mj4k1JpRraTFhXhw1QUbDpDIXfL5I/jqTZm0jCcfKtr0ffdnfjVBrKhJCU\nhNdzcuAbMlpzVji+K6Ewm7HJfPAB2tnejufhcECo4cTHwRrWpib8zevF33fswHwYyRx34YIQ/A8c\ngJn0G9/A/GhpEZUR9HoIbyzIqVTisHT99RiTo82XxhpcgwHXrq29OjJYrYYgxmNZp8O92u1oY08P\n2qPV4nc2IRPhtcceG/r7g/1ec3NxcMnOHl5j6XTi2Wq16Gerlegv/gKHv1CuDUwggP7kAuCJCrtj\nVFWhLzwe/Nu582pzOfevWg1Tqc2G5xfqUFxWhnFsMmH+cIAVs24dhOJwx9rq1UT/8z94Jm430csv\nQ0Di9SFUFOpEE8qHmdm1C2P8zBkcVIxG7AEvvST6c+XKgWlvOHWI3S4OMcuWYY4lEiMKbvfffz9Z\nrVb68Y9/TLfeeiv19PTQj370o4lom2QMaLXQaPh8MPOE42s13KLKLFiAjWr27InzQRlc/7O9HRtD\nVxc2KI4qamoSqR8uXw6dPiHYf02jGahhrK4eWpjo70cbjEZ8R3DQQXCqg0BA5JXT6yFgcNLTWCZ4\nDEdQYg1MUhLuL9R4mTEDi96sWWPzbwxVpSMceIweOCBScdhsIsnshQswac6fL/o6LQ0bqUpFdOQI\nNHVJSQMrbAQLthoNBLPgzdPthinvb/8WfWCzYWPo7MR1gs05bN7hMTaWfGnXXgsNjEqFNphMV7/H\n4xFm46wsCD6dnfhuzkFnMuG58fj/yU9Qr/Sxx9AXQzH4MOf3o89Pnx46MttshvabiOjWW9H33/kO\n5sRwXHMNrl1aijnf0oLrr1uXOIlRiURppU8+gUDR1YXn4/Vi3K5ZI94b3L/z50MIKS6+WmNmtaLv\nPR6M++bmq0vqRZqbj9cjvx9rW2bmQN9DPrTY7UQ/+xnG0GjSuYxFABxuTzp8GAc2pxPtVqnQL1VV\n+Ex5uTALM8ePD/QBJUKex3/9VxzKOL/bSMQ6V2FYghsR0bp162TFhASivBwbmFaLRXDDhoGnqdES\nXLNxonA6saB7PJgwvOkTCTMUmxB6e7E5DSWo8uah02HTrqrCPXV1iWz7oRxhnU4sPiaT8O3jBenX\nv4ZApyh4X38/rvfhhzDhzZyJv3EI/kTCDt/HjuHZ9/ZiPKhUYlO020WlCCIszkNFQ2Zm4iCQkzO2\ncRSqSkckNDXh8xoN2puUBMHz0iXca/AGyS4Abjc+s3s3BK/gAvA7duBvrCXiagnBZsF9+3Bw+fu/\nF1pevx/frVajT7Zvx/d7vUR33DF2jSsLhJzG4fhxbH7Bh6aeHoxbjwf9aTCIhMsOB54tv59zDCYn\nI5BiJII3To0Gn//hDzFWkpMhALJ2jDczLqWl02G+XH/9yEIbETRAWi3GxtGjGLtqNa4TrXkTvOF2\nd6N/xhoMMXgT37EDc+zQIfSX14tndPGiWLeY4P4dTmPGB6+WFhxO8vPHnt6HTYmNjTDxc/Qxa1N1\nOqGZCgSEyf1//xf5z8KF05Cw1jdazzI5GYcCgwHt4gTpHg8EOLMZB+dgYdHjwX2sWYO1n8vmXbiA\n63z96+F99/PP47Cu1SIYqaAgOvcULiMKbv39/fTKK69QfX09+Xy+K4l4H3nkkYlon2SUqFRYBLxe\n+C5ZLBBUli4VqRNi5ZgZ6WnF7YapMjMTmp6ODixgvPhyKaPublyXFxn2dwrG5xOnTKtVOPT6fDiJ\n6fWhfT0cDmGO7etDO6qr8fnTp4WGJSkJ99PVBSGithYLY34+FpCJhnNKXbwIgYSjIVNT0a5DhyCI\nffgh7sdmw8aRnh76eooCjc5Yx85wJpDh4LFTUgK/Rq6akJsLbQVrDC5ehLaKtUSKIkxWnN7myBGx\n4O7bJ4Sjmhpc32QaWP2ANWnPPouNgQUmkwn9ZTJBK9fTg/e//DKEt7Hg9eKg4HBgY71wAfcZbC5N\nThY+ZadO4f4tFlGn1O1GHxmNaO/s2aNzc2DtUFcX+rO9HcLfz36G51Jfj3VHpxPzrKkJzvr83Bob\n8dz7+tDHWq1YAxYuRBtNJggHdjvm02BhZywEa+97etB34dQ3Hi7idbA2e98+EUzCWh2nE68Fa3EH\na3nPncP7Tp/GASH4O+rq8Hd2BygpwRgfSyoePqDa7RByrrtuoO/h2rUikv7sWbSXE5BHAh8qQgVn\njIVrr0W7kpLQb0ajOFAmJ+P1Dz8cKCwWF6PPs7Lg2/kf/yGEvkjWITaDu91Ef/wj0d/9XXTvbSRG\nFNw2bdpE6enptHTpUjIYDFcEN0n8k5ODhZIXm44OCBNZWbEtGB9p4XqDQWyqb70F9bdGg013925s\naF6vWDw5soo1dHl5AzVPPp8wfVqtyIbf2ooNZ+XK0OaokyexWfNm2NWFTbKwEO9vb8fP+noszr29\nwpTHG0Ms/N243ziyj9PEuFzYMFeswLh45x1ssj09EIrHa4oPJ7SHkw6CN8lp00Q0XHIyrpmdjc8X\nFWET4rGVnIy50NyMccL+cEePwkyiVqNv2tqEmf3sWXwmJweajT/8AQJrUhLMf8HnVocDm0Z//0AN\nnduNjaOtjWjTpoHReuEeXg4exFhzOHAvZ87gWfn9oo9UKqEhdbvFoYUd4/1+PHeDAYlGzWZ8f05O\nZCk4uLJKUpLQPv/gB6Kdp08L/87cXIyl4mLc249/jCjG3l5oNWpq4FReXAxBQKtFe9PT0a6iIvRN\nXt7IprXh+pLdGGw2CJCnTqEvfD4cwth3cMmS4b9jOJ/hffuwLmi1MCM2N4tUQC6XCFJxOiGA+f3o\n5+AKEydOIIWFx4O8ZBrNwMCfjg6MV3bJUKkQBDGW2qJWq9C+Tp8ufDSZYNPrnXeiL2++WcyRcA/e\nS5bAOjF/fnTLrG3eDL/B5GRR6mrhQmjRWNBVqaDJZNavF/WsP/c5PIN334Wf2333hf/dfr9IoxJp\nIutoMKLg1tzcTO+8885EtGVSw4tLQwMGGGcRH08/sdtug2BTXi6cWSsrY18wPtLC9Q6HCJc3GDBJ\n583DhLVahRmMS/totXg/Vz8gEqHvZ86InD9ZWfjMpUv4TEcHNCZLl+JZ3Xab2DS44oLVik0nLw8b\n+MmTSAWyezc2NA7XJ8LGlZ0NTWFeHhb0iYYrJ2RmCvMvR7+uXCkWX5dLjEUuJB+K7m5sMD4fFu+/\n/uvI0gcEazymT4fmj9sQTkANB4mkpooNg0/0ajU2y+uuEzVmiWCqXrQIggc/o+5uzAk2qZaWihxn\n+/aJyOzrriN69FGi730P9/rb3xI9/fTV7eKDwJIluKbBAAGBq3QMzkQf7uHFYhEbPUeoHjgAwWDF\nCrTPbsf3aLVoc2+vSJPidoughTlzcNAI1nycOiW0ihrN8JsQt/l73xOaNqMRr507h2fDue3cbgiI\nmZnQUL74It6n1xP913/h2XCQR0EBxhJXpNi0Ca4d4W72wVVR2tsh+LFAwdU1jh3D/Csuxv3OmYP+\n4kTAVivaOxRaLT7X0yMqx2g0mDcffyw28cOHoY1vbsZY6+3FWNBoRFAPj+2WFvGsPv4Yfehy4RkH\nCxtEGLus5eWIUvbXGum5DcXu3egXux3r43AHy8JCmEezsjD+gn2OR/J13bABa3V3NwIH9PrIDzKh\nDhgajbB8GAxCe796NZ57czNeC74vbgubo7/5TWjtt24N37+NCHP5T3/C2mEwQAM3kfkHR1xyV69e\nTSdOnKCFCxeOf2smMby4sDkhJWVk9fxY4eSrPh9U3suXY9OLRZbo4IlXVoaFKNx2pKeLhbK2FpPO\n7RYnxqYmYaJhzQSbLru6sCC/+io2losXsTm3tWFyz56NZ2Gx4JopKZjoDgf6jDcN1pilpaHNc+ZA\nCM7MhA/PDTcIkytvgllZuFfOhB7KbBbtpKO/+x20rAYDhNbGRizKy5dDQOBKAqtXDwyquOYaCFUs\nqO7cic1lsFDmcqEf2W/HbB46qWgogv0Va2pEhK5KJTbH4RJHt7fDXMi+W04nzEWtrdgA9XoI01u2\niLG1apUwRQXnIuOqBFYr5mFfH/rpyBHh66hSQdDQ66F1Yx+smTOvTpmiKDC9fO5zePYul/D3GpyJ\nPtzDCye91WiwobF/YV8fxvDu3WgHb+Y+H+ZBWhrek5eH11JTcY3ubhG12NgIIWLw5jZSW9RqpEip\nqhJO51qtEC5cLnxvfj7G4Ztv4vvcbmyaK1fiOiwIHTyIw1hSEvq7oACa0OANlgjPyWIR2j6zGd/T\n0IC/nT+PcWOxYG7m5uKzdju+f/p0jJn+fvz/2DHMd51u5Ps3GHB9zhmoUqH97GfV0YHr1NZiM9+/\nH6NDr8EAACAASURBVPd//jzuy+sV2tAZM3DN7m48A9bYX76M8VlScrWrQm8v+orNx42N+L6LFyFI\nzpuHcRtJEMBHH+FeHA7cx3BVPoJ98Qb7HI8kuLHmjoX3rq6rDzKsWQ5VC5pIHLzZesGH85YW9IvR\nKPrZZsPfDQaxJwxuC6PTYf0+dSqyNXj9enznkiWwAnV1Ce33WOtBh8OIgtuHH35Izz77LJWWlpJe\nryciIkVR6MSJE+PeuMkAnyQ6O7GYcs4YlWpk9Xy0CB6ssTKPBmtTLlyAUBCu0JKejvfb7biXQ4cg\nDOl0UJfv2IFJW1WFvu7rw0J36RIW67lz8X+nU5grUlMh2DQ1YeHr7MRPiwUTf3DerWXLINjY7VgQ\nTp/GtefMwe8c4JCaisWjsBD/HnsM97l5c+hILPZBi9akb2zEz85OOOauWIFFcM8ebAjB+Z8uX8ZP\nqxWbZVkZFrC2Nphldu68WihLT8cm09eHvo40F3deHr63sVF8/6pV6Pv8fJyeZ88OLdBbrVjAm5tx\nf4GAEN4cDtybRoNN8913cb3ycuE7tWOHODixE30ggNcvXcI9LVpEdMstEDa8XiSDNRjQJjaFzZ9P\n9M//DDNhS4swD/b1oR83bsQzz8zEeLj7brTv4EGMn9xcbKzhHKKmT4dgkJKCzfXmm/GM0tMhnPC8\nCk5Fwn6erNXq74dw4PdDS9bXhz40GnHd//N/kK5jpKAjNjOnp6P/T54ULgkmE+6VfdJsNvTp7t0Q\nEJiKCvTHxx/jWVRVidyBajX6jE3YgzVtu3dj7nd1YT6yQM5CzcWLGJucwy83V5j4rr8ebTeZML4/\n+ADmydTU0FUIBlNdjX48fRrfMX061h+DAb/X16NfamtxPZ0ObePUMGwura/HOFizBuOffbBYIOZD\nIwcKMTqd8NfkeqZnzmANe+89jMl33xV+vawtHo6GBuEX7HDgOVgsEIiHG5M8h/PzR/aVDI4o5cod\noQ4y586hLX4/nt1gwc1mw2f37RMRutOmoQ9aWzEGmpsxT0tLcbByu9HGkYrHD67GMliZwnv4yZPo\nWy6TxaWy9u9HG7xe9F1Z2fgrRkYU3P70pz+NbwsmOaxp4xNwYSEEh5wcoQ0aij17MCBCqZbHg/EM\ncQ6lTQk31xxPvv5+LCw+HzZMux2T+c47obb2erGJJCfjmgcPYhN45RUssIEAFlI2G3q9WMT7+rCA\n2+1CE7pgAU7R3A833ohC8n4/FpF33sGJkSe9yYTX2WcmNRX3098fuqA29/UHH4gxUl4uBLfaWly7\nqQmLmNGIBSnYmXswtbW4rs2GPrvuOvzf4cB9VlVh0Vu9GouLXo+/azREf/mXIiIzEEA/hgrSKC9H\n/7W0oJ8vXYqsrBenk3nvPWycvHH092PjValEtvm8vIH3arGIpLINDWITy8gQwSkGA57b6dN4Jjyu\n5s5FnzQ3471ckeD4cXyv04nF/8gRbLyffIL+YpO73S40BBUVop7jK6+gvWq1+PzixRhbs2cjAlWj\nEW4SXC/XYMBzGImiIpFaQ6WCYPQXf4F7njcPQpzJhH/JySLZayCAzVKvx0+1WhwgPz1/U18f/vby\ny0gw/P776NOystCbN4/ZlhYIQzt34j5Xr0aaFM7f1tuLeXTddUSPPz7wGuvXY2586Uvo4wsXRJod\niwU/L1zAZjhY8LBaRdLsDz5AG1NSIPywb6bVij5IScEzLyyENvmTT9C+GTNwj+fOCa0Ra6KHorZW\n5AFkE11mJvq7sxP3f+QInonVinFjMoncYOx/29qKZ1leDsf6jg5RiYXN4ZmZEP6DtUR8DdYCp6fj\n7xwF7fVizDkcGF8eT3g1couLxVy32XCPv/89StgNV2M5kpRQBw6gnR4PnkNBAdFnP3v1XtbaisNc\ncnLodYcPjFYr7rmzE+NVrcac9vvR5xxBvXEj3rdqVWgz8m23YR82GIgefli0JziFFCsW3ngD1zxx\nAocl1uLywUKnE76ke/dinRhuX48GQ4oCDoeDTCYTmUJ5akvChk0inAyTHZl5kg5HaysWteZm+Abc\ndZdwOh5OuBqt+Y0dv/nkHm5S1HAIpU0JxzRGhEVk/XpM2u9+V5hNb75ZpN44cwZ/Z7+ejAxMtORk\nEchQUCA0RIoCwe78eZHmoKsLkz83FxqA9nbhw8GO6awlYgGLT4LTp4sgCdb8uFxDlwXjvmahIDMT\n12ZfCbcbJ7wDB/AsSkuxOJeWDv1snnwSgs3ly9CiVFYiV5dGg8WHF/tvfAPC5D/9E15PT0cizg8/\nxCLU24v7CyVYmM3Qdhw/jtOm0YjNJ9yxwhvfxx9DYCLCsz9xQpjiWlpQimmw/5fHA4GsrQ33mJGB\n1/r68HxYU8opWQaPq2XL0L9pabhudzcWbw7K8Puh1XG78S+4bFBqqnjmnBKHa6MSiRN7WhrMtP/v\n/0FYePttCKkOB67X1wdNwWAtwFBzduZMsbGzFubaa8XnFizA4SQ7GxsfO8anp+MzKSmipBULceyD\nqSh4fvv343lyWSZOXTNYO8/aoLQ0CE6trWiTyyUEsjlzcC/p6Xi+/Ey8XtwLV2Ahwsb3wQd4ptde\ni7q3BgPaUVd3teCWkyMSGxcXi2TKR48K/0Wew93dECyzs3GtoiIh9Pv9A/2fTKbQazE/k48+Eutw\nWRnmIZdTe/ppjDU2F7tcWIt4fPFBLiVFaPwvXhQCNldhWbBABMOUlgpzKsMaK71eJOz1+dCvzc24\nt5kzIfRWVBDddNPV9zOY666D0K7Toa179+K67MM7mMOHsSZ5vXApCIe+PsxtXue2bRNuDsF7GJdk\nMxhCJx9OT0e/6HQiEIzdK/r6RPUHDg6qrMR4GEq4rKvD91qtRP/2b0hxM3PmwHWMrSEffYTr1NSg\nf1kJc/kyxsOyZRi7ajXWroMHoWgZT4YU3LZs2UJvvfUWLVmyJGQUqczpFh4cQm+3C3Wqy4UFZSQt\nhV6PgeL3Y+B0dIiBOZxT82iqJhChbT5f+CWyIqGmBoP95EkxYcvLhV/KcMIllxcymURdRiIEEixe\nLMpRcd+wOUpRRDTlxo34jt27sUj19uJ1FrbYHEWEa126hD4uLsa1mprgm3P+PPqc/UfS07HxzJol\ntBmdnSKT92AG51XjCDFOeHngAN5XWIj3NjXhOjYb2tffP3TlhkOH8L0OBxajr34Vm/nOnbgnPl37\n/RBgnn8eAt53voP7P38eC2N+PsyLoXxkFAU1GHt7cZLlep2RwEEjbvdAQYsLpc+cKQSIYP8vsxmf\n5dQCTifupaMD/ajV4v+pqfgXXKvXaoXZ+Ne/xibd3i6CVUwm9I/ZjMPFuXNXayy6ulCU+3vfg7CR\nkYF5zGZnjUaU22EzbG8vnumCBRCWW1qwmYRy6D5wQHxncIHwtDQ8f3ZiZ4J9GUtK0AcXLmCcdHej\nPampaLfLJRLzPvYYBPxDhyC879qF/uDEupx+hPs9WAtvs4lEsBy5zebqY8dE1QajEYJwby/6lN0A\nnnrq6nxuX/4y2jN/PjY/Dt6qqYHpr7wc1zx3Du1nTU9qKtpx9Cjuu69PBAOwJorT3nR14XWdDmOc\nkxW7XBA0zWYIU34/hFhOoM0pT157DWPNYICwyb55Fosot6TV4r06HdrO/mwsOGdmCheKoiLM7aws\nPN/+fqwzK1ZAAFu5cqC5jrWI7O7B9aQ5IW9aGoS1tWvhy7tqFcbdSFaTzZtFuTPuK/YdG8yePUh7\ncfYs+u/MGURhhip5F2werazEodBgwHPYswf3PdifLSUF82coITo/H+vaokW4/urVEFg5urWvD33O\n/qsj5Rs1GNCvbjfm5vvvC40fW7fsdjwznQ73w1VZLl+GImX+fKzPf/mXQqvd1oYxPN4MKbi99dZb\nRERUX18//q2YxLD26vBh4YfBZZtGcojdtAmDZNky8ZrDgUnGoc+hCFeTNZi8PGzgmZnR94UL5dAa\nSTJfjkbs7hYCrNeL/lmxQpS+0umwsHR1YQF77z2cXk+dwuJWUyOKdvf1oU0uFyYlC37ssG40Co0Z\n1z/lxa2+HicxpxPCqMeDhdXtxrU5PxSXWHG5IGT+6ldY4Fta8P/8fGw4XM6GF84NG/CdTifezwlr\n3W4s1IsXC4d1Xpx5w1QUaBftdmzMnJKANYFNTWhrXh6ux47urDXKyxveL6SyUpTy4eoTkZhLWZuj\n1eK5padjA21txQZWWIg2D65nydUc+vtxr2o1rqFW4x8Lb4EABG82PxHhFHzpkhDUOa9bczM2DRZ4\nOXqZ/RE1GozRmhqRhPmmm5DSor4ez5rfl5ODzZCj/5jUVGxeRUXCofyDD/AZoxF+cx99hOfi92NM\nzJ8v7j09XQiJ+/ZB0/zHP6KtPp/QznBeQt7UAwHhIsC+O7NmQQB96SVoBlNSsHmfOoXn/8Mfishz\nooHRrx4P1oann0ZbOzsxdnU6zE+DAc+PtdAqFQ4Gu3ZBg7p4MQ45Ho8ILCgshPaGNd0uF/rltdeg\nqZk5E6bh6mrhGsEatd5efH9/vxAm2cwenEuONX7z5uG9druogcmm0wMH4P7w3/+NzxiN+FtGhqip\nq9dDyPzOd0SQRHW1ODg4HOif+nqhCcrOFhGY7Afb3o41/fXX0c6ODpH7racHwuVbb+HwxclljUah\nKbXbhTY4NRUHg/LygS4e1dW4psuFsfQP/3B11KRGIw6IbKJdvDi0tm7/fsyfkydFRoShKl/W1eFg\nyHWNidA3Hg++o7YWAq/VChPzT36CMRcIYB0Ipb3Py8P3paWJ9YfXCTY3JyWJgLSR2LULkbuLFkFr\na7Ph37Rp+NvnP4+9s7oaB1W9Hvf/5z+LUnvHjuF53Xgj/NW5H6dPH/n7x8qQgltVVdWwH1wyUZ71\nCY7VikXyww+xkLB/iV6PzWC4U5FGA18QjmLizO8+n3AQDjXIw9VkDYY3wUjKmYRLJA6tobBYsBCa\nzfidzTZuN07dM2Zgwubk4J4NBizWubnYlPr60N+cb4kj0xQFKviuLkzcQACfMRgwiW02fKfBgH5X\nqYST9v79EOaOHhXlYFQqkYD3yBEIRHzCzsnBQpCcjAnv82Gh5CCH2losQu+/j2uzr5DBgOucOoW2\n8AKfmYmFhwvbr16NDWjWLPyu0WDjYY1lXp4wz6ak4JrPPgsBl81JioLPDDduVCpsBh9+iMWYI1XD\nNZfm52PR5ZP33r14NsnJaEdXV2iHcY7m7e/HvfACzQ7yBQXCjN7VBX+du+/G5+x2CAJsAvX5hBDX\n0CD6hU1RHKFaUICF/dpr8X61GmlBOOiE0WoxJtgHk0lLw+e6uoQgevIkTKhs8mYzGGsgT5+GGYvz\nMOr1YpyazbgvriDCju9Ewk2ACGMiEEB/ms3Y0CsqICT98z8LTVxVFTYjInz3c88NDApQq9GnLKS7\nXNjAsrMxnzmSNTgoiAV57r+HHsLh6uBBbNjNzbi3a66BlqirS9QdLiwU47u6GoeZ6mpce+ZMIRCm\npws/tk8+EcI7R+oyej0ivo1GaCaPHsVnucJEXR36pboamrvLl0VQQXc3+o79y/jw8/LLGA/HjolA\nj/x8IfyznxVrbTifG/thdnVB45mZKdapjg6RVoT9qH72M2gkP/wQY5d97Hp7hR+vyYT5/uCDeE5O\nJ/r16FEI13yAS0uDBp5hzb/JJIqyL16MQ0QoX2qLBT9ZIeBwYByEQlFEMAfjcmFcvPwy1tf6elyn\nqwuHmkWL0J5p065WGrDWl0hYHaZNwxrwzDMYM5yANylp6OThwaSlIWnuq6+K+cmlIbu7EQGr1aJd\nn/ucOPDW1uK72afabkc/fOELCGhatw5C33gzpOD24IMPkqIo1NfXR0eOHLmSDuTEiRO0bNkyOsA2\nHcmwWCzYiC9cwKDgjPHsLDySfxC/v6VFmLM4U/pQhZh50Th2LDI/N5dLqOGjnaB3rDVOnU5sYhzB\nxuWpiDCZVSr0VWcnBBKnE5uUzydOZPX1WHAMBmGO4sWYtZ+cUb2gAAsCC5lqNQQa9jPp68OG4XCI\nhLtpaSLiyenEpO7sxGbQ3i5U7w0N6NviYiwyycm4nteL67DTOAdPsFaStVwcum42i5xSbjfuNzMT\n2qCjR7FZarVE996L+2trw2dYe+P3Y5MuK8NC1dYmfH6Go7YWG/6pU/i+3Nzwi4Lv2AHtc02NEKjU\namEOys7G/W/fjkWQIzDZbFtQgEW2oADfbzRCg5WejufC6QCOHsVYa28XNTVrasQBSKvF77x58MbC\nmkavVwQCzJoFf8B//3eiv/kbmIgefXTgODYasXkHpx/QaPA9OTno94ICtK2mRpjmWlvxemmpOMix\n9oEIggtrM9PTsXlwnjKOSHS5cO98KCQaaBbXakVKEIsF/cwO6R98gDHKfx9c6i03F2sOa3y4CkNv\nL8YJR++xI77LJXKLpaaizatWQXjbu1fkWUtKwmdsNsyzo0fRz0ePwpLw1ltoI2uYu7sx/jl4iE27\nN94IbSU7iNtsIjVQaSnujaNWKytFNLJKJWoLs98Zp1XhaGmTSRzweLyw0MRCMY+nvj5cLzkZc0Kn\nE23k5MxEWMerqjBf+JqcnoUTODc24p7vvhttOnsWP51OMS5ZQDca0ZazZ9HnFgs0ojqdCMApKro6\nOryhAdYI1urn5sLkN1QAXEWF8Nnl5LMdHQO12szq1TgAeL3oV3Zz6ejAs+B+LCzE5ysqhAWqvV0E\nhTEWC4KALBbMg1tvFfk72UJkMGC8ZWeH9pELhUoFodXjgXUrJwdBC088AWHM6xUuNkRo77e+BaH7\nyBHhF2u3o00+HxQt4x1ESDSM4LZ3714iIrr99tvpN7/5DVVUVBAR0alTp+j73//++LdsklBXh43b\n5cKE4/xOdjsmY2Xl8J/n/DmnTwtfoLQ0UdB6KEZKrBkqgjTSxLiRMJRZ1GrFouN0QvAaXOqF38PJ\nR9PSRD+yozQXDOdIrkAAC+6xY2IBZSd2zppeWSk2BnYa53tnE86yZQNNbUeOQABXq4VPGgsU7DB8\n+fJAE6zfL/w5NBpsdGlp2FR8Piz0Xi/eo9WKTY/9wJYsEfnHgqPv7Haiz3xGCGBsPm5uxum7thYb\nZlKS0Ip4vdgUZsxAv5jNGFe8OXFgwoEDiDIcilOn4Mx+6hQ2q7Vrw68KUVODzau9XZRO8njQV5wE\n12ZDH+7ejT7o7kaKDjYBHz4sfH56e0UiZd7Y2ITX2Ch8fdatg2O8yYRnlJo60C9KrxeJnLOzRT6t\nZcvwvQ88gM2W07pwAlVGpxMm+eJiEVHMzvBOJ07uXEeVF3yjEc+ATf0zZgg/HSKRmJYIBw9OQM11\ncTMzRWQvC68uF/qBhbaUFFy3uhr9Gexj1dsrnLoXLYKAGgxHSLMZvbAQffHcczC1XrqEZ8mVJzo6\nRMFvh0Oktnj2WVECqq4ObWK/qtmzRXR3crLIUcaCJydcnjUL13O5MAcsFhxWioow7ru78b38HI1G\n9DGXAXvvPfQPR3+yoKVW41lfuiSqSmRni0OM0QhtDLf/k0+EWZrdDziyPDUVz5+1sZwiiIM0urpw\nfxcu4Dv0emhwWAtsMgmN+LPP4jBw9qzQbLMPIfev1yuUAvx/PpgmJWF83Hvv1aWx/H5ofrkcHK9f\nQ8F9XF6O7+OI8rNnr/Zzy8+Hn95bb2EtLSnBM+juFlHcGzagP1auxPz84AP0KZfhChbcOjqw9nq9\n6MtTp/AaW1w4d+dwPnKh+Pznhe/iTTeJAx2vLRwQxH7iioL9yWzGPP7zn0VUdnU1vp+r04w3I8qG\nZ8+evSK0EREtWLCAzpw5M66Nmkz092Oh6O3Fw+/pwcRVqUQtxXAIBETUGREGzHDClcs1sBj7YDiq\nsbERi0RubviJcaOVNsRqxUTeuRP319uL0/bs2QO1LBaLmKTsw8aLKvskEeFegxeh1FRMzKQkUY8v\nOxvvKy/HBK2uxk/2VdLp4BuiKDhtsTN1ba1wOicSUXsGg8gUn5oqtHtE6H/OZp+RITQ6vHE4HCKi\nMdj5fNUq9Mv27dhw9u/HePF4sDBwOouGBpwAOQP7o4/Cb4YFuBMnRGoBbhPn2srLg3ajrk5kNedN\nsrsbJohHHw2dTZwFA4cDY8XnE6XFRsJmgzDS3IzPsZMwCxzJyfhXW4t/bB5sb8dGc+wYxiybOlUq\njEHWQHBONa4ty2YTtRq/c+QxC7MpKcJvqq8Pi++0aejTLVsQyU2E5xic1uXVV4V5njVr+fnog02b\nIGTl5orgouZmon/9V1y7pwffywW86+uFD1RzM97D/cl1PXnsE6EPCgrQZqtVbGipqegrLt+m1+P/\nev3AUktlZfjckiUw7xiNIt9csNaOYd/XggKMrcpKol/+En/77W/Rtxcu4H6Cffu8Xjy/X/7yan+o\nnh4cSNRqmOgaGkSUX24uxmhfH37PzMR7srKEkBIIoA8PHRJjkUiMA48Hz4YFHiLh68dBS2zOXbIE\nz5L9t3JzcT1FEcEhnZ0QWjmhLt+nWi3MnexmsG4d2pmXJ+p7sqaMDxcNDcJnUKvFtTl4TVEgqNTW\nityLioJ/vO45HELz6HYT3X8/0S9+gWfLbiULFkDIvuGGq9fosjKseyx8pqcPL/AkJaEtej2e85w5\nQvs9GHZpOHpUaEBzciDosWa7rg7abE47w/WneT0IhmtPcw7O9HRc22gUwv369ejD+fPDd9koKIAp\n+gc/wCHR6URaG7bclJRgbLAvXnB93VtuQaBFdzcE0YsXcS+5uUgBNN6MKLgtXLiQ7rvvPrr77rsp\nEAjQiy++SJUjqYkkV0hPx2AyGKAheeEFcSo8cwZOu+GQliYGKRdJH8rHjQhC4tmzGGihNF0dHRio\nDQ1oX3Bi3JEYXFR5tGlD+vshOB09KhZjznml04m0B3y64vxXHNHHdUA7O/GPKx9w9vjgOqFmMz6X\nkQHBYPZssYkPri/p8+EeH3uM6PvfF2Y8jqjjpJrLlkGIKClB3yUlifYTYWHJysKCyBnh1WqRhsFi\nERFo7Gzb14fFYN06mBAOHxaf4VM1+5i43dj0WcP4yCNioeT6pF4vhNTqahEQ4HKhf1jjxWYW3vx6\neiD0PfMMnMsHw35FycnC3BTuKbeyEm1xuXCf3Pec04r9VAIBYebJyIC7wa5dmDNsouE+Yd8w3sw8\nHrFJ19YKbUNenqjdy4WleQxwNKqiwIm9vBy/P/IInuE3vzlQiE1NxeFizx6RxoADWaZNE1oxIjwT\nvR7Ci8slqj3k5eH1ri5sWhoN7n3lStGfc+Zgw2MBLzgfYVkZnh1n/58xQ1Qo4BQ3HPzCGpif/lQE\nCB05gu9ISkIE7lCBTCxAqlS4B0XBfe/di+8LrhMcTFMTvvftt4ceD/X1ME1dvixM14GAOGCxIF1R\nIYIOjh2DVos3+4YGoelj8xX7os6di79zolyvF8+TLRhaLQ4AmzfjfkwmzDnOhM9Cg88n5h7P14UL\nIThyKhpO+3Tp0sBgJz7ssY8arx980NPpMM75IMImUw7EWrcOQp3RKCowJCWJ+6+pweFuwwahdVOr\n4cu4bl1o1xQuR8amy44OrCHD5R9LSsIade21WMtmzIA/XSj6+6EhffNN9I/fL4JEjEYkIGZ/tuDo\n/8zM0D5qXIWCzcqKgrWALRgbNuCzg1N6DMXjj0MITErCXHW70ccNDRAoly/HnCgshDaQU0EVFeG+\nKypE+hm2BtlsGM9cRm08k/COKLj99re/paeeeoqefPJJIiJau3Yt/d3f/d34tWgEdu7cSQ888AD5\nfD6677776CGOZ45TysthZquoGGiC1OkGluUZCdb8aDQYPKE0acGasAULRMb3UBPXZBL5zrjOY7gR\nqCz0sVp7tLCzrl6PdrMfD7eL+dOfYOYgGlgiiUP8Oes4+6plZeF11lCmpmJyp6cLVTproDixI/cn\nCzwuF/yZiCBglZZCQ5CUJAp8m81Ck8MpSTi4g/3dbroJCw4HOgymtlaYhTj4obNTVGSorkZ72XmW\n/dwUBdqZ3l5hkp05UwgG06djw503DwsZaxW5b9nXj7UkWVlCIGQTMB8QBo8fNi9yCDwHhISD2y3u\nJ1g7w/6KvICaTHhGPEZsNjwnrt0ZjNcrAj6Sk4UA19ODOcEpA5YuFQIn+9RwIILLhQ2mrg7Pbto0\nYf5oaoJp8P77B34vC3pcDJ3n8tKlEEaCzcc33ihqoLL5sqICmzyX4crOFlHEfK0VKyAkmUxoY2Ul\nTvcWC0yblZUw2fT3Q4jjNiUliajJ7GyMVb9/oMN48DPh+q6hiodzcfnaWiH0vvAC2tHbi+/kAIpg\n7r0X93fhgniNzd0ccVlXB8GDAz9yc7G+ZGdjXnFaDzYx1tejj6xWPJcLF4SjPZubuRarzQYt4jvv\noI3nz2NeqNXCz9Nux3U0GqIvfhHCgMWC7+Pk0CxMejxC28/+lGzu5YOCxYI5x+4L3Oecd+3/s/fd\n4XGeVb7v9KY6ozKj3izLRXKTe40TOwRsh3VIiLP3JoRs2MCySy6Bvcuy7MLu3ocL3F16YB9uCCG0\nQNiQC9kEp4c4cU9cZNkqlixZvY66NBrp/vHLj/NpPFWWZNnoPI8eSVO+7/3ect7znvM7v6OUEMky\nyuDzQfcSCsJycgUF4nGLj8d1OjpwTWL+KBcv4jpk7V+7VjL3gwkhIfTkNzSEzhJVSrLeExJQhcLv\nh24LFl4ltMVsxvXr69HfeXnQkVu34t4uF+YCQ8bM5g2mJ4mZbGiAXkxOhv6i3jQYsJ6iLaNINrP2\ndswr0hNdugSqHRq2ly/D+ExMxDwrLMSel56OuaSFS4yNgcLmllvgqYtUCuxqJKzhNj4+rm677Tb1\n6quvqk9/+tOz14ooxe/3q09+8pPqpZdeUpmZmWrt2rVq3759asl0KuzOkbjdAgj3+3Gye/11AEE1\nEeiIwtp8ej0mWmHhlaHSwHpv4bxnHo+4tIeH8TvazZdGH7MyAyXaUGpaGjaX8nJs1mVl2MCWOmA9\nCAAAIABJREFULp3adoZhmP3DGog2G65N44ceGL0ebSAeJCtLyC0JJu7vx+YWHy9Fpq1WoZS44w4J\nG/l86J8335Q+GhjA9QwGCa/09ooR7HBgcZvNUKI0PigtLbgGT2cM3QwPQ0k9+ywoEUZGsBG43TAI\nqqvl1Mo2e71Qpgx7TkwAX/LuuzBAfvQjwVmNjuK+k5P4TmOj9CvxkwRUnzkDZR1YhovEosSY9feL\n0RxJadpsmDcE8Op0uBYNN266DHG3t+OZLl/GnH/pJfGqMVu2t1e8ksR2EbvIWo5KwVPwH/8hoRCb\nTcD2zIwcHIQHuKEBG01cHJT6iRMYD7sd/elwYPzj4oRglc++bNmVmL/Vq4VnLyMDiQ7Hj8P4TklB\nO1lDk1nTSgnR7MCA1GLkfL1wAWuFdRlZ8mliAs+2ciW8Hjt2SNanyyUbNCsx/NVfAbzu92PMdbqp\nXHOk4mlpwfdZaoge2vFx9BXLFZ05A29kf79S3/2ueJ70eniA1qzBunv3XTH+iME0GmE8Xr6Mtnk8\neK26WsobDQ9jvGtr8f2uLqmaop1/bW0gQu7oEJqd+no5HJrNmItMrNmxQ/Cz5Is0GLCZM1vcasX4\nXb4siQxKCbWLXg+Dn0lGVqvgm0dGxHM5OorxHxuTrNPt24WgeXQUUI2TJzEnaPQoJYZ+oHz0o5i7\nLpfUcg4na9Yg5D88jOd97jkYVcGMMbcb7WxsRH+E8uQpJWXO+voke50HzYQE8fDb7biOTif74/Dw\nlR63vj70J7NgGxrkcE04AHHH0YrdLuHqO+/Es5Ovkwkr3/0u9qbTp4Wfs6UFBhtL9LFCCTGgzNQn\n/+dsSVjDzWg0Kr1er3p7e1VSNDm2syxHjx5VRUVFKi8vTyml1N13362effbZeW24EffQ3Iy/N28W\nripmvEVTDcFolGLrzOwJDJV6vVITr7Y2POaI4Y/pFJyn0edyBV8s0YZSdToAtg8fRlaOywXFSiAw\nr028HjO6SJablgZXfWUlFDs9FcSdkJ6AXEbkDWOZpKoqKbrtdmMMLBZ8Vvtcbjc8H88/j+/SOG5v\nl1MisRpMIGANRYYDtX08MgIMGw0FhgjI/s5QJz9bUYHTbW8vnpkEw319EmbhyZEYnoceArbl17+G\nx4abEKWnByDfhx5S6gc/gBFALiQSAisFY5CGGw1ykuBy83rhBYwZuczCSX097k3OOWZdklCaWXIj\nIxLWPXQImWoMLw0PiyFDbkBSUNDgIe8YPaGtreLF6++XcRsZwZxiltrgIN6nR6S8HID9/fuFkPVT\nn4JBMDkpBKqJiTCS6AnSitkMj11uLjatPXvwXNygiZdsacG87O3F5s81kpgoGd9eLzLfuOFWVgqR\nKwm0mRzgcGDe0BtYWanU//k/4Gp7+ml4o370IwlNvvSSJMxoi4efPCn8Y0ePStiQwpqQXV3CDTg0\nBKPK68X89Pngtf67v8O8PHMG33n3Xazn+Hgx4LSeJGYdMxTOELdeL4kR9NaTykUr3d0SqlRKQo18\n5u3b4e1iVQBmLHNOkVaFh0J6/0ZGsOZobMTF4XWLBc9jtUrCFXnYeDjh4dLlwpolZIGVLYjb5OGA\nxNQ0bul114rTie8vXoz78wBw5kxoVoF9+8Chxj69fBnjG1gjVimQdf/Xf+Ez2dl4vsWLg+8xb76J\nsenvF91E7yT7n0b45cvAhJLSiGX4tHLpEj7b2Sk8jdwvW1qQSKUtWB+N/Ou/KvXAAxiXjg70Fx0S\ng4NYmwYD9CLJhMkT6PPBqNu8Gf1QUgLjjhAPlh2cTYkYKnU4HKq0tFTt2rVLORwOpZRSOp1Ofetb\n35rdlgWRpqYmla3hHcjKylJHjhyZ83bEKsnJ4v6ny7yiQk62xL4oFdrQYQUGl0uwOQSn0thLS8NE\nI2dROA8Is1WnI5GMvro6wSGEYvmneDw4nTMzMD//yqzWTZuwCRAYS2B5SQk8YaTT8Hrl5ONwyOZv\nMmHD3LZNSDt1OoTNCJgl3QdJdJubhacqLQ3jdPvtUF42G5QtDdRFi3BSrayEAmfYQqfD9YxGGBrM\nllRKQMuTk+JBowJ1uaA8SM/BjKaBASgXeg9JFcGEFWZH0jDxegWUrpQkb/BE2d8voOumJjH8PR7M\nn+zsqeTPzzwjrPTMfB0exsb+m9/AqxRJSEWRmiobUE8PFCNxicQBeTxoj7bOakmJhDNZno0AfXqk\niKXR62EQXLqE56yqEroQcnMxXMX+Y1/R8GhtFe8sPTfvoUb+aPw5HMJD9ZWvwNAkz5ZSwho/NgYj\nze+Hx02nEyoOhqaVwmbb0YEx0elg8LF6yt694oFQCvekJ4hl05QSD5C2HrLfj3675x5wZx04gOf7\nwQ/w22bD9VNTp3ItcjxMJnwu0NvDg5zbje+xTYmJ+Bkbw3v/8R94Fp0O3nWbDdd79FHhZ2xslOsy\nyYRhSZJuk16Cc5FGjxZfqr0G54PWOCotxQHt9tvx+ytfgdHS2Yl5RoodetDppebasVjwnsWCsbBa\nxSM2MoKx0NJCcO3Fx6MtrBM6MiKlwzo78Z3ubvTp4sXSFuJVtTpKK0uWYA4ePYprcF2xEkVg4XSl\nYKC6XFJpIzUVB9Rg8sormKssTB8Xh0NzMMONOD8atPRWMRzOxCKuLasVBvzgoHjKtVEblg/z+USv\n9vVhLRUUoJ9ipeCwWhEJ+d3vZG4xQ91oBDTn/e+XOrba/mb5ruefh14oKsJ3urowvnv2hMb+zZRE\nfNz9+/er/fv3K5a9mpycVMFKYM2FRHvfL37xi3/8e8eOHWrHjh2z06AohXXWJicxWUk2OTGBBaz1\nGIQSGlodHTil8MRiMknZkO3boWBWrRLQ/kxXQNC2JZSMjIjnidlxzzyDTcViAckjjQ1m0507J+EL\nLWu8UlBiixZBaVy8CGXMovAdHVKDsL1dNvxlywDYJSYtPR19Q2NOKVHqzESkch8dRd8xI5DG9Jo1\nMLhJxKh91q4uKEKSqep0UjidbejvlwxQZhSSSJSUGHFx2MCSkvC8ixZhXnR2YkyNRmx+pHIYGMDm\nyOvxM9xsaBQoJRlS9fX4v74e5NB/9mcAZrONHg9CcB/8IPqKbPdHj0o4kt4ucs9FWybt/HmhM6EH\nmQaczyf8dWwLs2JZccThwGv01pAuxG6XGqTEgzoc8vfkJLwqSkkomKEthlmJ2+Q6ZMF0lwueqU99\nCkbbe+dXZbNhg+a1ioslxMb5m54u48Nrv/EG1i/HxWyW8C3Z30+dQn+89ZZk+1osGIuVK3HNxsap\nSRVMxGEWKisxUMjVxnHs7cVB5OBBSQxSCka41kNTUiKHEpdrKvGqxYJrstyXz4dxJcB++3aM06OP\niiePG3JxMfjdnn4az60UXtd6k+hpYuSCXsXBQbSXWcicj4HY37ExeEbOn4defPdd6IaNG/Gzfr0U\nEK+owJqhcc3xKijA9202tKevD6+RQ4xVYQIJZ10uOeQwsYyZsklJ8mx8Hp9PODrJP6mUvKfXY24Q\n98lMeNZj/va38ZnCQqyXr39dqKCeeEL6mKLToS/IfPDBD4amBKmpEWwz9brWM6Ytc7V4Mbz8DAlz\nXOmtYnIZaVIOHZIMV5YKY3ie65JcmUweIt5OpwteoisaYdLO8LBk2ycmKrV7t1Lf+pYc7JKSpGwZ\niYiVEtqWhARknB87BszpRz8anlpFKdCtkXJtOhLRcPvIRz6iRkdHVVVVlVJKqZKSEmWaDWr9KCQz\nM1M1ao5kjY2NKisIOl5ruM0H4Ym0qwsTl1xfzHRUCht6qNCjVrgRX7yI69hs8Jikp4sy5Ek0VEms\nQNEuOi0Nx3SFipus3EqJt2NkBIr6wAH5fEuLGG5MqabXsbtbQmjsKxoPjY0I97EWITPuCIR1OrGY\nmD7v88Hg0usRWuWp3WYTcDVBsMPDchrmFNPrsbBbW/E+jS2eWKlIlRIvXns7rkeDk6d0egK1bN0M\nk/h8GM/8fCjL3FzhmGNNP7sdCpmu+dZW2dzohWD9wieeQJtqanBNltHx+WC8HTmC69JLNToKT1tu\nrngVubkMDmJMCwowVuS+Sk/H5peeHp7wmYauTidGMb2O3LRpTNGYpqeRxrFeD8/ApUvCCs/sLhrm\n3GRPnsTfrDFJ6gYysDM0SUoX7eEpL08qLzgcCI9qpaVFMljXrkW7nE6pyJCejrG32XBvzt+Ghqkn\n+NFReMEOHhTcJMHwJ0+KgXzpEjaGgwdhWNOAamoSbJTWy97bK6TGaWlCUkuams5O8WArJQz+get/\n+3ahDImLAwN/YqIYOST/dTqx4dKL2N8PIzAhAaHdm2/GAYFGIg+cDAcyE5YJCRRu2Myop5c0IUFC\n5uSsJK2LVqqqxAvk8WBer12LQxETipgFyqQnJkwRF0vjiOPW3Cw8edrEAwqTlnioIF8eyWvPnxcK\nHNaiNRjw/IODWKdZWdBTDCMajZi/S5YAk6WUYLyIHzMaoWsMBqxtGjd/+7egC6F0d+Mw3deHcSU1\nRyih04A6SgtLUArchH196IeMDKHZYRicdZuZrKHVl8nJGEfyKtKrSRgED/ec2yQC1tbLnY6sW4e5\n2tmJsOkTT0gyy4svip6/+WZEspiEphUSObMSyIED0VUeCnQofelLX4qp7RENt9dee03dd999Kve9\nAlwNDQ3qiSeeUNu3b4/pRjMh5eXlqrq6WtXX16uMjAz11FNPqZ///Odz3o5YhNgZ8o/RI6ItAl1S\nEj3ejBlGNhsWAZm6te/394v3IZrUaO2iM5mQqn01smwZFIc2NXtkRLIVA6dOZ6eAs7lAKS0tUJJD\nQ9iIzGahs0hLg8JyOPA/vVfEsyUnwwCprsaGZTbjevHx8EY4nYIN4QasBaoTq0TjqLNTQN7EylFY\nWJ6hbILqicvTcqkNDaFthYVoE4ul22wYt4kJwb0x6YFhG7dbvC85OZg7r70m/EYuF1579VUYw9r5\n1NcHTw5Do2xLays2UIb2yMS+caNs4o2N2EhqauClUAqen6EhhAqqqtBHDMUtXx58bvA5xsflkEJe\ntrg4tIFjTMxdQoLUE2UVgLEx4Z8j9QMNPXoEiYUkaN7hEEOFGbPEJrGoODdxux2bXTg1R/xUQgIO\nEKtWwTOXny+4vZoaXHtiAv2UkjK1VJZSAmBnFQISdHu9MucIdj95Uoxa4m64vlgpglnNrINJqg0e\n+tatg3cpPx9gdkpiIsI/gWIwYC4cOoT+uv9+4FLJX0dqlrQ0GGgsx0U4Q1cXNsW338b1mdmdmop5\nl5ODNqak4FrJycBJab2FXJc09lnjlq8xlBhs02RovLkZ81LrpeOcLC5G39LoZ+F4GloeD/q4p0cI\nvXlI5GGH48F1mJIiVRCGhgTXyQMsy5URJxkfL6W4amsxfzMzxQNJw5JeVsIw2FZ6rF0uIeTmIeKB\nB6b2ycgI+pjwDNZyDSUkJmZVCadzauZ/Q4Pw0C1fDqP8mWdkrOi9Ukqy2DnnOjslKYnk8unp0EEO\nB/qNnnOlcC0ecP1+GKjBst8jSVmZZJ739CAi4XYLNQrngt+P+V9QAH1TVSVVMfR6JBtWV6P/cnPD\n64yZkoiG26c//Wl18OBBtfi9NL+qqip19913R6xlOhtiNBrVd77zHXXrrbcqv9+vHnjggXmdmKCU\nAGeZMk5G9okJ8UjFgjcjHxQzMFesmLrguroknGqxCE9OOCEAk1lfkSRS1qjbjUmvNUQ3b4anYMuW\n4HgEglkZQqZoQwwGg4BdMzOl+HdvL0DYBNifPIm/y8uF7JIUEcwCslqF34uUDEpJuJR0FcQTkkX7\n3Dks0kAsDZVmU5PQTPAzWg+AwyGhV2K06NGiIly1Cm361rdAXvrUUxI+7OrC2KakwD3/ne9gE/Z6\noTSGhoTOgCzzWqFxS1JVhtRWrJDwR10dxmrxYoyXXo8TKBUzQzkmE4yFVauAy6KX5fTp0IZbYaGw\n5NP7Q+OQRhjxWzSMDhzA62++CUOIoHf2HTdAbThS6z2yWvGTkiJlc0iNQu8QubEYil63Dl6IcN5n\nGooMtXB8t29Hwga/q9Ohrw4dkg2XQkOU2cIEZ58/L6WyLlzAdTs6kAH35puSsUhP5ZIl6PPhYXyX\n+Dtm2CqF+zQ24nt5efCMnDiBOdHbi/kWLgettRXX2rgROueVVzBXdDp8r7kZn2FCQVycGKQvvogQ\nLb0sSUnIvktKwrUOH8bn2T8JCQLwJn+YXo/xY4bn5KTQR7Dvguk6ru3MTNyrpARzqKIC/bZkCQ4b\nJLq224XFv6NDMFDLlmEN0DvEgxkNNlKFrFol1VN4UOF64WvE0jFKsGiRUPrU1IgeWb1aQouk2EhK\nwvhxviuFuaLTSY3il1+Gx31oCAeQQOpVgwHrnmO1YkV4w2frVvz2eND+NWumOgWYLMKSiWvX4vBI\nLjrSJGm57chhSJzo+DiumZ+PudzQAD3FJDDyWNL5wRJXZjP2gXCGZzAhCf5NN8nBnIfY+nqpgpGR\nAT04MoIM7CefFNz0wIDo5EuXMF7zwnAbHx//o9GmlFLFxcVqnObxNZDbbrtN3RZrCsk1FG0ZqSVL\nJFvrZz/D32NjwBZojZlQhlFtLTZIrxeGm9+PDUa74FjSh/xs0eDcaBBEazxGyhoNZoi6XEhvD0Yh\nkpAgmBJiy+i2J8ErSU4nJtCP9Lb09UlR9P37AYDmc7/xBk7SNATpBUxKQoHht97CptjSIopEKUlg\nIHGuw4HszKYmKHuCtQOFWZDBhEqLVCZ08xMfZrOJYTE+jtNdaysU2OOPy3XIkdTZiTDwW2+JYVhX\nB+8FT58kMtaSDA8Po5/Jzk9lePKkeH6Gh6GE6uqk2DlPpizVlp2N/nK58LuoCN/NyIBxEUry8mDY\nXboka0MbZuLf9LAwy7W8HPdlFmJTkzDTkw6DtSyZCajX47OpqTC4y8qwfhIShPFd6wFh1mlysmyE\n4SQpCe0IJA0tLlbqt78Vg2l4GOPe0nJltpnNJvx5Q0N4BnqXf/97bGK1tWJ8Vlbid1ERftPjnJGB\nzYu5W3wtJUV++/04CBD8TT6/pCSEeSJ5LFhmKSEBz3L4sIQT33kHbWW/0ptZVATamd/9buqBbGgI\nWaxDQzh0kUuP9XRTU4XRPyVF8KXEhJFOgt5qpTDXtSFWrcTH4yByyy1o/y9+gec5eVKpz3wGRs6m\nTVJtoaMDc8Pnw2+LBWPi8Qh20OnEOmTkhOvhrruwube2CsUPf3Ndsq8J8UhOxrWZdETP2uAg2vXW\nW2Kcsi7u4cNon9eLfvvABzDHWad56VKskf37rxzbtDTo0QsXoGsiRWaWL0e7BwYwTlu3TjWUiBdl\nXdG0tCtDuNTf9JKyYD1hPno9DuZeryQWtbfLgZHeVC2Ot6wMBnm0BOCBotMJdKGzEx7j3FxcLycH\niQavvoq+Tk9HJi4ZDf7+76fiPdvasOfcdFPw0o0zKRENtzVr1kypnPDTn/5UlWtTzRYkrGizQWnQ\nEKNB17vNhkVH0QIztXxKb78N5cVw6c03y+QINPbi4nCvQK43klVqi89nZ+O75MOJJNEkUwRKOAoR\nj0cIdP3+qZsgM+FMJtnEmcnEBU6qgpISwY0MDuI69ISZTNjMtm7F6fb229GfDJMyrZ+ldMbHYSSs\nWoXxa2qCUqKhQcyGUgJy1dIGUFhImsBipaQoNZMJGGpi35JDLDERrzN0oxQ+S6OE4TMKEyN4gq+r\ng3JuaIAC5PcZpkxJEX48LXiY4RXWoOTm0NyM52lrQ8iVmD+dDifSy5cBzA2H8ViyBJ6XykrxOCYk\nyHwnjog1F7u6gMGbnIRx8fTTssmTloTzXilZD1T0o6N4v65O6iMS16YlEmYYiiSnly7BEIqE+3G5\n8KNV0mVlVxrwd92FxAbt3MjJES9wTQ3axezhwEw2nw/z0u2GkUGjjqTCPT3w2jU0SJWE1auxqWVl\n4XpM0CG1BvticlIONK++GhrnyjmXlATDhEZae7vgLbXS2gpP1mOPTe0PerzNZhjxvb0SxmZCCAmH\nyS/JzNKJiamhNha+V0rWVKDQ28ps+yNH0N/Etr75JsZszRrMyzNncN2ODvGckwJldFQIrZkQQqPD\nYoGRbDKJR5vjSx1B/UDDgwk0+fn4DMHubW3QZ3ffjTG3WhFmbmhAG30+SXpQCu08dAhzymaDTs/L\nw74SzIDQ6ZBB7Pfj3vHx4cONpaVYt04nnj0vb+pnuQZZa7ilRbLbuUaZaasNdTKjnlhB0uMoJQTJ\nDE0H3mvpUozbqlXTq97DPZNULYS/1NdLKcOqKsAgqNs7O9FvpaXot0DqnyNHYLyZTFfWB59JiWi4\nfe9731Pf/e53/0j/sXXrVvWJT3xi9lp0g0kw7xOBtlR878EH/2hUkSldp5tacJcJCX19WNRao4xh\nuaQkwTkEC5PydMGNraREvHYlJcE5fLRCr0tbG07T0RajD0chwsy7igpc/+BBwf2Nj8MIqajAYuKG\n096OBdfYKNgkjweLqaUFz04vTFERnjs5Ge+Vl+OkyVJBBOtzs6Lx43JhIVNhU6kwcy4+HoYFiXEH\nBgRLREXocolXg8oqIUHAt/398h6rGYyMoH3/8A/Y/NLS8Ds3VwwtgpgpDofgtbq6kGE2NASD5Ze/\nFJwcywAZjZItxyxELQ2LxYL5+PjjmIPE8Ol0eNakJCFP5Zi8732RgbkVFRK2Hh9HX6SnY16T6oHJ\nISQ/ZQbk2rVTDwykgKChw02bOMmUFMyThAT0e02NKGeG2Vg3lKGx0VHM7aNH8T2GnoIJPX3Er1IY\nVqLY7TgEaLm8Nm7EwenQIXy3slIysWmI6HSCQ2PdVd7nxRfxm15Ch0OoOlJScOInnIDrTafDhltb\niz5mdZUPfhD9+/bbUrR7374r1ykLrDObmzIygs2TRe+Zbbh9u2QEpqRg0ysowDPxIDAwgH5g39Ag\nZ3k6Zslykye27PJlMW75OkOW2mQBs1m4B8+fV+rf/x0GLUHtaWnCU7hjB8aqogJ9xGQyPiO9vCwX\nR2OUXjOW6Nq5U2oF87mZQGO14pnMZrzX24s1t2MHjK7XXsPcOH0a2Y1ut4TzCWUgN2VgQkRmpnjl\nqIvCeX127lTqD3+Q+sxm85UF4yl6PcaytRV6TVtKTik8F3GDDQ1iUPNgrJSQ6/Lgpddj7THkz+sE\n1j8ldRGpj3i9piasbx5MYhU6SPbsweGC+p4OBJI119dL6Lu9HXvIX/81DjhnzwrptVK43sGDOCTO\npkQ03KxWq/rkJz+pdu/erZS6tlmlN5IsWwZFuXy5nBbOnsUkZcYRs8Qo6enCXp6bi0VMo5BhJ71+\nqrIOFC3mxekU9u64OJAYRvK48fRJwzPaBAjyWLW3X4mL6+nBBkQm89ZWbEy33Sabc0aGMOm3twto\nmOniZ8+iLatX46RDyomcHChNux0LvasLGwJ5d/R6KEdu4FYr+pfeMYYltm+HAWS3i+FGviV605gF\nZbWKd5D3ttvFq6SUGBs01mw28YTGxcFgaWqS8N+iRfCKsKZnbi48A0w0KCiAIcVN5W/+BgbE4KBg\n/0wmbNwscu/3C0EojUAqRgLriZkkiSY3q+5uwYmdP49neU9FhBXi6uhFpZFGo2l8XGqCOhzY2Hgv\nZkMSfO9wiKeTXiZ6DTmuDoe0mRs8aRg4HvS0kiC0vx+vvfoqQnwf/GDwZyE2R1um59FHoehJAaIU\n+vvUKay31lZs0sySZhjO7xfsotMp/G0mE16j9+LIEWyuJKulR8jjmZqgYTaLl1srP/85Cml/4xuo\nZXnffZi7X/86DPKuLsy9zk68pzVa9Xp4EwLJX1ldorAQZaP27cMadDrhEWF4MS4O9960CSHKb34T\n34uLw/ytqUF7W1vFUGVGNcu7KSX6qqtLEiE4D1gTk0YNDRd66ior8T+Lqh84IMlY9fXYdH//+ys9\nKYST+P04CNKAZmJCcrIYiN/4BqAYTz0lHJ52u2THsxKLwSCe9cWLscYPH8acWr5cEmzo+e7vl2oB\nhHBQlzscSBrKz5eEoVtvDT5vKcw8fe45MUq+8IXQBxUaM3q96GD2r9uNw3ZBAShm6FlLTISO44GW\nWcBMiuB7rNPa1CR4YmY9KyXODm3FleFhQCA6OsKTzYeSN9+EDmX0i3vt5CQwf6OjctCkdHbCC/fc\nczB8ly/HMx47JlUxeDCcTbmuskqvF4mm5FNhoXjUOPnPncPE6O3FAtC+pxQWZmsr3OCDg9gMiJ0j\n3idSZioLUzPj87HHZNM5ejRyRind2XFx+IlWQoV/lcJmxE3T4YAC2LUL7/EUm5Qk4UmGBMfHsSlc\nuCCn7EuX0G/kPurokDqNzO6srEQ/tLZKqSxmplos6BuGJMnBRvoLeoq6utAWGmpUmORt4vcZoiLp\nJAlgvV7cb2ICr2dmyiZO3BMTAWw2KAieoouK0PaREdybGYzMrFMK7x06hPlEJeJyQZlrudOYxUXD\nzesVI5OUD21tUnbJ7xfDgiHUgQFsRIEn5WBy8aLc3+MRzw03ICan6HQCeid9B/F/DgfmaVOTFAnP\nyMDG94c/SF3HtDTMhcuXMXZHj0omoNmM59TybA0Oynpjgsnx46ENN1KqaMv0XLqE8d2+Xan/9/9g\npHR3S5WIrCzc98IFfJabPWkQGMYk6W5HB57XYsHz2Wyg2ODa8PvF48BDDiuBBHpbqJe+8AU82623\nYpyPHcNmxDqwQ0PwhvHwRCFmMFBYDzk3F0bg3/0dNsSVK9Ger35VqYcfhv66+WZ45+LiYKQohbFj\nJjrvTyxUXR3WT28v7kGjm0kcJG1mMgRJuCnj45jH6elo5+LF2IwzMjBn9+6Vw+rZs5JkoxWGN5mN\nOjqK61RWCs6N+LrTpzGPXngB40ISb27+IyMY69xcvMfDqtOJMdu6VQw9Zp83NEgZNI8Hn+f+4nJh\n3ZWXY77fcguuu2tXeFJaRnfq6zHHbDbMgcAxpxw/judubJQKIIcOYR1yH2tshJ6qr4csEu6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6HI9u7F5tPXB0X1P/4HTj27dsHoobL46EdRTaGmBkpj82apJ7h3LxYdww0ExtvtuO7EBDYackCx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\ntf1j+PlJyQTL9kizZkkG6syZ4T2BfSCio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       "text": [
        "<matplotlib.figure.Figure at 0x10d2cec50>"
       ]
      }
     ],
     "prompt_number": 11
    }
   ],
   "metadata": {}
  }
 ]
}